Mojo is a new programming language developed by Modular1 that aims to address the performance and deployment limitations of Python in areas like AI model development. After demoing Mojo prior to its launch, Jeremy Howard from the non-profit research group fast.ai said it feels like coding will never be the same again. Here’s an excerpt from Howard’s article: Modular is a fairly small startup that’s only a year old, and only one part of the company is working on the Mojo language. Mojo development was only started recently. It’s a small team, working for a short time, so how have they done so much? The key is that Mojo builds on some really powerful foundations. Very few software projects I’ve seen spend enough time building the right foundations, and tend to accrue as a result mounds of technical debt. Over time, it becomes harder and harder to add features and fix bugs. In a well designed system, however, every feature is easier to add than the last one, is faster, and has fewer bugs, because the foundations each feature builds upon are getting better and better. Mojo is a well designed system.
At its core is MLIR (Multi-Level Intermediate Representation), which has already been developed for many years, initially kicked off by Chris Lattner at Google. He had recognized what the core foundations for an “AI era programming language” would need, and focused on building them. MLIR was a key piece. Just as LLVM made it dramatically easier for powerful new programming languages to be developed over the last decade (such as Rust, Julia, and Swift, which are all based on LLVM), MLIR provides an even more powerful core to languages that are built on it. Another key enabler of Mojo’s rapid development is the decision to use Python as the syntax. Developing and iterating on syntax is one of the most error-prone, complex, and controversial parts of the development of a language. By simply outsourcing that to an existing language (which also happens to be the most widely used language today) that whole piece disappears! The relatively small number of new bits of syntax needed on top of Python then largely fit quite naturally, since the base is already in place.
The next step was to create a minimal Pythonic way to call MLIR directly. That wasn’t a big job at all, but it was all that was needed to then create all of Mojo on top of that — and work directly in Mojo for everything else. That meant that the Mojo devs were able to “dog-food” Mojo when writing Mojo, nearly from the very start. Any time they found something didn’t quite work great as they developed Mojo, they could add a needed feature to Mojo itself to make it easier for them to develop the next bit of Mojo! You can give Mojo a try here.
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